Event-Based Approach to Multi-Hazard Risk Assessment Maryna Zharikova1[0000-0001-6144-480X], Volodymyr Sherstjuk2[0000-0002-9096-2582], Oleg Boskin3[0000-0001-7391-0986] and Irina Dorovska4[0000-0001-9280-8098] Kherson National Technical University, Berislav Road, 24, Kherson, Ukraine [email protected], [email protected], [email protected], [email protected] Abstract. This work presents an event-based spatially-distributed dynamic mul- ti-hazard risk model for the objects of critical infrastructure. The multi-hazard spatially-distributed risk model is based on the five-level spatial model, as well as the dynamic models of the socio-economic system, vulnerability, and event- based scenario model of multi-hazards represented on macro and micro levels. Each hazard on micro level can be represented as a sequence of events plunged into a certain context, where each event can initiate scenarios describing the multi-hazard dynamics. The risk for a certain object at a certain time point is a combination of the following components: the object state, disaster threat, the vulnerability of the object, and the potential damage. Thus, the area of interest at a certain time point will be characterized by integrated dynamic spatially- distributed assessments of the multi-risk in the conditions of multi-hazards. Keywords: Multi-hazard risk, Model, Events, Scenario, Hierarchy, Critical in- frastructure, Socio-economic infrastructure, Socio-economic system 1 Introduction Economy and society in the globalized world are increasingly dependent on the relia- ble availability of essential goods and services provided by technical and socio- economic infrastructures. Industrial facilities and critical infrastructure are vulnerable to the impact of hazards that can generate cascading effects. Different sectors of the infrastructures are interdependent. Being under influence of hazards or multi-hazards such interdependencies extend affected area and increase damage. Climate change also gives rise to the increase in the frequency, intensity, spatial extent, and duration of extreme events [1]. Disaster prevention and mitigation require analysing risks from hazards and multi- hazards, as well as their cascading effects to critical infrastructure elements. A subject matter of such analysis is not only single hazard but also chains of hazards. At that, one specific event can trigger different possible paths of hazard chains. The effects from hazards can also be cascading. Cascading effects are associated with the level of vulnerability and interdependency of critical infrastructure objects being at risk. The chains of events are usually represented using event-tree structures, also called event trees [2] where the nodes are associated with the events, and the arcs are Copyright © 2020 for this paper by its authors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). associated with the conditional probability of the next event occurring given that the previous event occurred. 2 Related works Much has been done in the field of single- and multi-hazard risk analysis [3]. The classical definition of risk maintains that risk is a probability of occurrence of an un- wanted event multiplied by the amount of loss [4]. In disaster risk case, unwanted event is disaster that can’t be represented as a single event, and disaster risk analysis can’t be assessed using classical approach. There are several reasons for this. At first, unwanted event (hazard or multi-hazard) is dynamic spatially-distributed process spreading in uncertain conditions. At second, disaster risk is analysed to protect some objects of critical infrastructure influenced by disaster that are also spatially- referenced and can be interrelated [5-12]. In recent years a range of approaches has been developed to disaster risk analysis. They are as following: quantitative (deterministic and probabilistic), indicator-based approaches, risk matrix approaches, event-tree approaches [13], data mining ap- proaches. Quantitative deterministic approach allows considering only one individual hazard (such as landslides, floods, wildfires, etc.) or a small subset of potential hazardous events and can’t be applied to a wide range of hazards, as well as their interactions and cascading effects. However, in real life most of the regions are prone to multiple hazards that can lead to cascading effects. Quantitative probabilistic approach is that risk is assessed quantitatively taking into account a given set of hazard scenarios and the probabilities of their occurrence, at that each hazardous scenario is treated as an undivided whole. However, probability is associated to frequency of hazards, and the researcher faces an issue where the event of interest is quite rare. To cope with this issue and to increase the representativeness of posterior statistical samples, large territorial entities and big-time intervals (10-100 years) are considered [14]. In most cases, probabilistic approach is based not on imitation of many thousands of events using Monte Carlo method, which is connected with high computational complexity. Indicator-based approach allows to carry out relative holistic risk assessment di- vided into a number of components such as hazard, exposure, vulnerability and capac- ity. The relative risk assessments don’t provide information on actual expected losses. The risk matrix represents semi-quantitative approach to risk analysis focusing on categorizing risks by comparative scores. Such matrix is made of classes of frequency of the hazardous events and the expected losses. Risk is represented as a combination of these two dimensions. Existing event-tree approaches allow analyzing disaster chains. The nodes corre- spond to hazards, and the links between nodes depict the situations when one hazard causes another. The main drawbacks of existing event-tree approaches are as follows: each hazard is treated as undivided whole without referencing to spatial locations. Data mining approach to risk analysis work well in conditions of incompleteness, inaccuracy, ambiguity and uncertainty of both the initial data and the rules for their transformation and can be an important tool in finding the correlation or uncertainty of risk factors [15]. In spite of flexibility of data mining methods, they are character- ized by high computational complexity and can’t be used for risk analysis in real-time decision making. Currently, individual hazards and risks are analyzed and treated by disaster risk managers separately, especially, natural, social, and technical risks are not combined. A severe gap is a fact that critical infrastructure is often recognized as important but treated only regarding its technical components without representation of the popula- tion and its vulnerability. When the vulnerability is captured, even in multi-risk anal- yses, it is done under static conditions, not in real-time. This emphasizes a knowledge gap in understanding the dynamic interaction of destructive processes with their po- tential receptors (the elements of CI, people) [16]. 3 Materials and methods Giving foregoing literature analysis, some gaps in the state of the art of multi-hazard risk analysis can be distinguished. Individual hazards and risks are analyzed inde- pendently. It’s necessary to accumulate the knowledge about dynamics of multi- hazards, their interactions, and their cascade/simultaneous effects on CI elements. Risk assessment is also usually represented as a static value. We propose to con- sider risk as a spatially-distributed process that provides a more comprehensive un- derstanding of the multi-hazard risk concept. Spatially-temporal approach in multi- hazard risk analysis provides critical information on hazard areas, impact zones, and location of populations and vulnerable infrastructure within hazardous area. Thus, the objective of this paper is to develop a model of spatially distributed dy- namic multi-hazard risks for CI elements on different time and spatial scales, includ- ing cascading risks and risk-related processes driven by environmental and socio- economic changes, based on the models of socio-economic system (SES), multi- hazard dynamics, and vulnerability of CI elements. 3.1 Underlying models Taking into account both temporal and spatial distributions of multi-hazard risks, the comprehensive approach to multi-risk assessment proposed in this paper is based on the dynamic models of the socio-economic system, vulnerability, multi-hazards, and spatially-distributed risks. SES is a complex dynamic system resulting from the interaction between people, environment, technical and socio-economic infrastructures, which represent their interdependencies. Technical and socio-economic infrastructure (SEI) is a dynamic spatially- distributed system of systems consisting of the elements important to the activity of people and society. Critical Infrastructure (CI) is a part of SEI containing elements which are essential for the maintenance of vital societal functions. The damage to critical infrastructure, its destruction or disruption by natural disasters, terrorism, criminal activity, or mali- cious behavior, may have a significant negative impact on people's security. 3.2 Spatial model All the above-mentioned models will be grounded on a multi-level spatial model (Fig.1). The lower level represents a system of geographic coordinates. On the second level, the spatial model is discretized by a grid of isometric